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arxiv: 2309.12802 · v1 · submitted 2023-09-22 · 💻 cs.SD · cs.LG· eess.AS

Deepfake audio as a data augmentation technique for training automatic speech to text transcription models

Alexandre R. Ferreira , Cl\'audio E. C. Campelo This is my paper

Pith reviewed 2026-05-24 06:58 UTC · model grok-4.3

classification 💻 cs.SD cs.LGeess.AS
keywords deepfake audiodata augmentationspeech to textvoice cloningautomatic speech recognitiontranscription modelsaccented English
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The pith

Deepfake audio from voice cloning augments datasets to train speech-to-text models with less real labeled data.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Training robust speech-to-text models requires large diverse labeled datasets that are costly to assemble, especially outside English. The paper proposes a framework that generates deepfake audio via voice cloning and mixes it with real recordings as a data augmentation technique. Experiments apply this to an Indian English dataset that contains a single accent, producing synthetic audio from existing transcripts. The combined real-plus-synthetic data then trains transcription models under several different scenarios. If the approach works, it reduces the volume of new real speech that must be recorded and labeled.

Core claim

A framework that uses deepfake audio produced by a voice cloner on an Indian English dataset can augment training data and support speech-to-text model training across multiple scenarios, thereby easing the requirement for large and diverse real labeled collections.

What carries the argument

The data-augmentation framework that selects a voice cloner, generates synthetic audio from an existing single-accent dataset, and mixes the output with real recordings before model training.

If this is right

  • Augmented data sets allow training of transcription models in varied scenarios without collecting equivalent amounts of new real speech.
  • The method provides a practical route for handling accent-specific data such as Indian English.
  • Synthetic audio can substitute for part of the effort and expense of creating diverse labeled speech corpora.
  • The framework is validated by running existing voice-cloning and transcription models on the chosen dataset.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same cloning-plus-mixing step could be repeated across additional accents or languages that already possess modest real recordings.
  • Performance gains might compound if the augmented data is further combined with conventional augmentation methods such as speed or pitch shifts.
  • If cloned audio carries systematic artifacts, models could learn to overfit to those artifacts instead of general speech patterns.

Load-bearing premise

Audio from the chosen voice cloner must be realistic enough that adding it to real data improves transcription accuracy rather than introducing artifacts that lower performance.

What would settle it

Train one model on the real Indian English recordings alone and a second model on the same recordings plus the deepfake-augmented portion; if word error rate on a held-out test set does not decrease, the augmentation claim is refuted.

Figures

Figures reproduced from arXiv: 2309.12802 by Alexandre R. Ferreira, Cl\'audio E. C. Campelo.

Figure 1
Figure 1. Figure 1: Voice Cloner Architecture (Real-Time Voice Cloning) [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of a step in the process of generating new audios [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Illustration of the step-by-step performed in Experiment 1 [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Illustration of the step-by-step performed in Experiment 2 [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative analysis of the retrained models [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative analysis of the models After the last qualitative analysis of the models, it can be observed in the visualizations of [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
read the original abstract

To train transcriptor models that produce robust results, a large and diverse labeled dataset is required. Finding such data with the necessary characteristics is a challenging task, especially for languages less popular than English. Moreover, producing such data requires significant effort and often money. Therefore, a strategy to mitigate this problem is the use of data augmentation techniques. In this work, we propose a framework that approaches data augmentation based on deepfake audio. To validate the produced framework, experiments were conducted using existing deepfake and transcription models. A voice cloner and a dataset produced by Indians (in English) were selected, ensuring the presence of a single accent in the dataset. Subsequently, the augmented data was used to train speech to text models in various scenarios.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript proposes a framework for data augmentation in automatic speech-to-text (STT) models that uses deepfake audio produced by voice cloning. A specific voice cloner and an Indian-English dataset are selected to enforce a single accent; the augmented data is then used to train transcription models in various scenarios, with the goal of reducing reliance on large, diverse labeled datasets.

Significance. If experiments were to show that mixing cloned audio with real data yields equal or better transcription accuracy than real data alone, the framework would offer a practical route to address data scarcity for accented or low-resource speech without incurring the cost of new recordings. The controlled single-accent design provides a clean test bed for isolating the effect of synthesis artifacts.

major comments (1)
  1. [Abstract] Abstract: the text states that 'experiments were conducted' and that 'the augmented data was used to train speech to text models in various scenarios,' yet supplies no quantitative results (WER, CER, baseline comparisons, or ablation tables). Without these metrics the central claim that the augmentation mitigates the need for large labeled sets cannot be evaluated and remains an untested premise.
minor comments (2)
  1. [Abstract] Abstract: the nonstandard term 'transcriptor models' should be replaced by 'transcription models' or 'automatic speech recognition models' for consistency with field terminology.
  2. [Abstract] Abstract: rephrase 'a dataset produced by Indians (in English)' to 'an Indian-English dataset' to improve precision and readability.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the single major comment below and agree that revisions to the abstract are warranted.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the text states that 'experiments were conducted' and that 'the augmented data was used to train speech to text models in various scenarios,' yet supplies no quantitative results (WER, CER, baseline comparisons, or ablation tables). Without these metrics the central claim that the augmentation mitigates the need for large labeled sets cannot be evaluated and remains an untested premise.

    Authors: We agree that the abstract should contain key quantitative results to allow readers to evaluate the central claims immediately. The body of the manuscript reports WER and CER values along with baseline comparisons across the tested scenarios; we will revise the abstract to incorporate the main numerical findings (e.g., WER reductions achieved when mixing cloned and real data). revision: yes

Circularity Check

0 steps flagged

No circularity; empirical methods proposal with no derivations or self-referential fits

full rationale

The paper proposes a data-augmentation framework using existing voice-cloning and transcription models on an Indian-English dataset. No equations, fitted parameters, predictions, or uniqueness theorems appear in the provided text. Validation is described as running experiments with off-the-shelf models rather than deriving results from the framework itself. No self-citations are invoked as load-bearing premises. The work is therefore self-contained against external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical model, free parameters, or new entities are described in the abstract; the work relies on existing deepfake and ASR models whose internal assumptions are not examined here.

pith-pipeline@v0.9.0 · 5663 in / 965 out tokens · 23309 ms · 2026-05-24T06:58:57.960926+00:00 · methodology

discussion (0)

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Reference graph

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